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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5553 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
A fuzzy logic based generative explainable AI for automated video auditing ↗
Abstract Video auditing can provide vital insights into complex industrial operations where quality, safety, and compliance are critical. However, there is a need to develop explainable artificial intelligence frameworks capable of recognising workflow steps and producing interpretable reports from long, untrimmed video sequences. The key module enabling reliable auditing is action segmentation, which remains challenging due to ambiguous step boundaries, fine-grained action similarities, and uncertainties caused by environmental variability, multi-object interactions, and class imbalance. To a…
- OpenAlex · Published · First archived
An Exploratory Pilot Study of Cognitive Factors Influencing the Adoption of AI Coding Agents in Agile Development Teams ↗
This study considers the cognitive factors influencing the adoption of artificial intelligence (AI) coding agents among software engineers in Agile development environments. In this context, Transparency refers to the degree to which an AI system’s decision processes are clear or understandable to its users, while Trust denotes the confidence developers place in the reliability and correctness of AI-generated code. Specifically, it analyzes the roles of Transparency and Trust as predictors of developers’ Behavioral Intention to deploy AI-generated code. Using a quantitative, cross-sectional su…
- OpenAlex · Published · First archived
AI-Driven Decision Support for Systemic Innovation ↗
Artificial intelligence (AI) is changing the scope of decision support from model-assisted analysis toward systems in which algorithms, data, domain knowledge, and human judgment interact throughout the decision process [...]
- OpenAlex · Published · First archived
Electronically Rotated Electrical Capacitance Tomography Sensing and Image Reconstruction Based on Transformer Model ↗
Electrical capacitance tomography (ECT) is a noninvasive imaging technology based on capacitance sensors mainly used for measuring and imaging nonconductive fluid in multiphase flow. However, ECT has underdetermined and nonlinear problems due to limited sensor electrodes, which pose challenges for image reconstruction. Therefore, this paper uses the sensing technology based on electronically rotated electrical capacitance tomography (ER-ECT) to increase the number of independent measurements by combined excitation to alleviate the underdetermined problem. At the same time, ER-ECT combined with…
- OpenAlex · Published · First archived
TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry ↗
Artificial intelligence (AI) has transformed molecular prediction and design, yet these advances remain only partially integrated with experimental discovery. We define TechBio 3.0 as an emerging paradigm for closed-loop drug discovery that connects multimodal molecular representations, generative chemistry, and automated experimentation. We examine its early preclinical and clinical evidence, identify remaining technical and translational barriers, and argue that this convergence is beginning to define biotechnology's next era.
- OpenAlex · Published · First archived
Non-Destructive Imaging and Structural Techniques for Reliability Assessment of Power Electronic Modules: A Literature Review of Conventional, Automated and Machine Learning-Based Approaches ↗
The rapid expansion of electrified transport, renewable energy systems and high-efficiency industrial power converters relies on the robustness of power electronic modules and multi-chip packaging architectures. As module power densities rise and operating environments become more extreme, reliability becomes a key determinant of system lifetime, safety and cost. Thermo-mechanical degradation mechanisms such as die-attach delamination, bond-wire lift-off often initiate within material layers, making non-destructive testing (NDT) essential for quality and health monitoring. This literature revi…
- OpenAlex · Published · First archived
Ecological Invisibility in Classroom AI Use: Rethinking Pedagogical Efficiency, Sustainability and Teacher Agency ↗
Artificial intelligence (AI) is increasingly embedded in everyday teaching, yet its environmental costs remain largely absent from how educators reason about its use. This study examines how 22 teachers working in public schools in Türkiye, each with at least one academic year of experience integrating artificial intelligence into the classroom, interpret and navigate these technologies at the intersection of pedagogical effectiveness, professional responsibility, and ecological sustainability. Semi-structured interviews were analyzed through Braun and Clarke’s reflexive thematic analysis with…
- OpenAlex · Published · First archived
Integrative Bioinformatics Analysis Incorporating Artificial Intelligence and Machine Learning Identifies Putative Pathogenic HNF1B Missense Variants Associated with HNF1B-MODY ↗
Maturity-onset diabetes of the young type 5 (MODY5 or HNF1B-MODY) is a rare form of diabetes caused by variants in HNF1B. Patients with MODY5 exhibit high blood sugar levels and symptoms commonly associated with both type 1 and type 2 diabetes (T1D, T2D), along with kidney and pancreatic issues. Nonetheless, diagnosis, treatment and management approaches depend on specific genotype–phenotype associations. Interpretation of HNF1B missense variants remains challenging because of conflicting classifications and limited clinical evidence. This study aimed to improve the interpretation of HNF1B mis…
- OpenAlex · Published · First archived
Dataset-Calibrated Perturbation Budgets for Adversarial Robustness Evaluation of Machine-Learning Network Intrusion Detection Systems ↗
Adversarial evaluation of machine-learning-based network intrusion detection systems (NIDSs) requires an explicit bound on attacker perturbations, yet fixed bounds are often reused across datasets without verifying scale comparability. We evaluate two differentiable detectors on CICIDS2017, TON_IoT, and UNSW-NB15 using a constrained, control-surface-aware evasion procedure in which perturbation cost is measured in interquartile-range units. The measured constrained feature-space evasion cost differs by more than two orders of magnitude across benchmarks. On UNSW-NB15, changing only the perturb…
- OpenAlex · Published · First archived
Modulation of Soft Magnetic Properties in Fe-Based Amorphous and Nanocrystalline Alloys for Power Transformers: A Review ↗
Fe-based amorphous and nanocrystalline alloys are essential core materials for high-efficiency distribution transformers, high-frequency transformers, and power electronic transformers, owing to their high saturation magnetic flux density (Bs), low coercivity (Hc), low core loss, and excellent high-frequency response. This review summarizes recent progress in the modulation of soft magnetic properties, covering composition design, heat treatments, structural engineering, and transformer applications. For compositional design, a central issue is how to achieve high Bs while retaining sufficient…
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How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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